{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from jqdata import *\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt \n",
    "import numpy as np\n",
    "import datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Draw_param(df,param,x='index'):\n",
    "    # 使用subplots绘制图\n",
    "    fig,ax = plt.subplots(1,1,figsize=(10,6))\n",
    "    # 绘图 使用eval（）函数 将字符串df.index当成有效表达式来求值\n",
    "    ax.plot(eval('df.'+x),df['000001.XSHG'],color='b',label='上证综指')\n",
    "    # 绘制上证指数 X轴为日期 Y轴为上证指数\n",
    "    plt.legend(loc=1)\n",
    "    # 设置图例位置\n",
    "    ax1=ax.twinx()\n",
    "    # 制作第二个图层\n",
    "    ax1.plot(eval('df.'+x),df[param],color = 'y',label=param)\n",
    "    # 绘制比列吐 X轴为日期 Y轴比值 设置颜色\n",
    "    plt.legend(loc = 2)\n",
    "    # 设置图例位置\n",
    "    plt.title('%s与上证综指对比图'%param)\n",
    "    # 设置标题\n",
    "    plt.show\n",
    "    # 展示图形"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 获得公司的索引\n",
    "all_stock = list(get_all_securities(types = 'stock').index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>000001.XSHG</th>\n",
       "      <th>pbcount</th>\n",
       "      <th>total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-12-30</th>\n",
       "      <td>3414.45</td>\n",
       "      <td>382</td>\n",
       "      <td>4137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-31</th>\n",
       "      <td>3473.07</td>\n",
       "      <td>374</td>\n",
       "      <td>4140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-04</th>\n",
       "      <td>3502.96</td>\n",
       "      <td>373</td>\n",
       "      <td>4141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-05</th>\n",
       "      <td>3528.68</td>\n",
       "      <td>376</td>\n",
       "      <td>4141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-06</th>\n",
       "      <td>3550.88</td>\n",
       "      <td>389</td>\n",
       "      <td>4144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-07</th>\n",
       "      <td>3576.20</td>\n",
       "      <td>406</td>\n",
       "      <td>4147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-08</th>\n",
       "      <td>3570.11</td>\n",
       "      <td>408</td>\n",
       "      <td>4147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-11</th>\n",
       "      <td>3531.50</td>\n",
       "      <td>429</td>\n",
       "      <td>4148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-12</th>\n",
       "      <td>3608.34</td>\n",
       "      <td>431</td>\n",
       "      <td>4149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-13</th>\n",
       "      <td>3598.65</td>\n",
       "      <td>444</td>\n",
       "      <td>4150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-14</th>\n",
       "      <td>3565.90</td>\n",
       "      <td>438</td>\n",
       "      <td>4150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-15</th>\n",
       "      <td>3566.38</td>\n",
       "      <td>428</td>\n",
       "      <td>4150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-18</th>\n",
       "      <td>3596.22</td>\n",
       "      <td>420</td>\n",
       "      <td>4152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-19</th>\n",
       "      <td>3566.38</td>\n",
       "      <td>414</td>\n",
       "      <td>4154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-20</th>\n",
       "      <td>3583.09</td>\n",
       "      <td>417</td>\n",
       "      <td>4157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-21</th>\n",
       "      <td>3621.26</td>\n",
       "      <td>421</td>\n",
       "      <td>4160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-22</th>\n",
       "      <td>3606.75</td>\n",
       "      <td>428</td>\n",
       "      <td>4163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-25</th>\n",
       "      <td>3624.24</td>\n",
       "      <td>447</td>\n",
       "      <td>4164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-26</th>\n",
       "      <td>3569.43</td>\n",
       "      <td>448</td>\n",
       "      <td>4165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-27</th>\n",
       "      <td>3573.34</td>\n",
       "      <td>455</td>\n",
       "      <td>4168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-28</th>\n",
       "      <td>3505.18</td>\n",
       "      <td>466</td>\n",
       "      <td>4171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-29</th>\n",
       "      <td>3483.07</td>\n",
       "      <td>481</td>\n",
       "      <td>4172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-01</th>\n",
       "      <td>3505.28</td>\n",
       "      <td>481</td>\n",
       "      <td>4174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-02</th>\n",
       "      <td>3533.68</td>\n",
       "      <td>480</td>\n",
       "      <td>4174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-03</th>\n",
       "      <td>3517.31</td>\n",
       "      <td>493</td>\n",
       "      <td>4175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-04</th>\n",
       "      <td>3501.86</td>\n",
       "      <td>507</td>\n",
       "      <td>4176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-05</th>\n",
       "      <td>3496.33</td>\n",
       "      <td>516</td>\n",
       "      <td>4178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-08</th>\n",
       "      <td>3532.45</td>\n",
       "      <td>509</td>\n",
       "      <td>4182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-09</th>\n",
       "      <td>3603.49</td>\n",
       "      <td>498</td>\n",
       "      <td>4187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-10</th>\n",
       "      <td>3655.09</td>\n",
       "      <td>496</td>\n",
       "      <td>4192</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            000001.XSHG  pbcount  total\n",
       "2020-12-30      3414.45      382   4137\n",
       "2020-12-31      3473.07      374   4140\n",
       "2021-01-04      3502.96      373   4141\n",
       "2021-01-05      3528.68      376   4141\n",
       "2021-01-06      3550.88      389   4144\n",
       "2021-01-07      3576.20      406   4147\n",
       "2021-01-08      3570.11      408   4147\n",
       "2021-01-11      3531.50      429   4148\n",
       "2021-01-12      3608.34      431   4149\n",
       "2021-01-13      3598.65      444   4150\n",
       "2021-01-14      3565.90      438   4150\n",
       "2021-01-15      3566.38      428   4150\n",
       "2021-01-18      3596.22      420   4152\n",
       "2021-01-19      3566.38      414   4154\n",
       "2021-01-20      3583.09      417   4157\n",
       "2021-01-21      3621.26      421   4160\n",
       "2021-01-22      3606.75      428   4163\n",
       "2021-01-25      3624.24      447   4164\n",
       "2021-01-26      3569.43      448   4165\n",
       "2021-01-27      3573.34      455   4168\n",
       "2021-01-28      3505.18      466   4171\n",
       "2021-01-29      3483.07      481   4172\n",
       "2021-02-01      3505.28      481   4174\n",
       "2021-02-02      3533.68      480   4174\n",
       "2021-02-03      3517.31      493   4175\n",
       "2021-02-04      3501.86      507   4176\n",
       "2021-02-05      3496.33      516   4178\n",
       "2021-02-08      3532.45      509   4182\n",
       "2021-02-09      3603.49      498   4187\n",
       "2021-02-10      3655.09      496   4192"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 获取上证指数\n",
    "df1 = history(count = 30,unit = '1d',field = 'close',security_list = '000001.XSHG',df = True)\n",
    "df1\n",
    "# 删除空值\n",
    "df1.dropna(inplace = True)\n",
    "df1\n",
    "# 获取破净个数\n",
    "q = query(valuation.code,valuation.pb_ratio).filter(valuation.pb_ratio<1)\n",
    "df1['pbcount'] = [len(get_fundamentals(q,date=i))for i in df1.index]\n",
    "df1\n",
    "# 获取上市公司数量 日期\n",
    "df1['total'] = [len(get_all_securities(types = 'stock',date = i))for i in df1.index]\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df1['lower_pb'] = df1['pbcount']/df1['total']\n",
    "df1\n",
    "Draw_param(df1,'lower_pb')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.7"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": false,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "MarkDown菜单",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
